Comparison of Probabilistic Quantitative Precipitation Forecasts from Two Postprocessing Mechanisms

Wu, Limin; Scheuerer, Michael; Schaake, John C.; Kongoli, Cezar

doi:10.1175/jhm-d-16-0293.1

Cited by 18 publications

(19 citation statements)

References 29 publications

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“…BMA (Sloughter et al ., ), GEV0 (Scheuerer, ) and CSG0 (Baran and Nemoda, ) are three state‐of‐the‐art statistical post‐processing approaches, as suggested by Zhang et al . (). Note that MPM, RAW, BMA, GEV0 and CSG0 maintain the same training‐test scheme for a fair comparison.…”

Section: Resultsmentioning

confidence: 99%

“…As the reference forecast, RAW refers to the raw ensemble forecasts without any statistical post-processing approach. BMA (Sloughter et al, 2007), GEV0 (Scheuerer, 2014) and CSG0 (Baran and Nemoda, 2016) are three state-of-the-art statistical post-processing approaches, as suggested by Zhang et al (2017). Note that MPM, RAW, BMA, GEV0 and CSG0 maintain the same training-test scheme for a fair comparison.…”

Section: Resultsmentioning

confidence: 99%

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Mixture probabilistic model for precipitation ensemble forecasting

Yang

Zhang

et al. 2019

Quart J Royal Meteoro Soc

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Statistical post‐processing approaches are widely employed to construct improved probabilistic meteorological forecasts from numerical weather prediction. However, generating calibrated and sharp probabilistic forecasts is challenging. In this article, a post‐processing approach, Mixture Probabilistic Model (MPM), is proposed to calibrate probabilistic ensemble forecasts subject to sharpness. In particular, the proposed MPM is applied to precipitation forecasting. First, the Censored and Shifted Gamma (CSG0) distribution is considered as the probability density function (PDF) for precipitation. Then, the predictive PDF of MPM is mixed by the individual PDFs which are fitted from raw ensemble members. Finally, to estimate optimal weight parameters for the mixture of individual PDFs, the Dirichlet distribution is utilized and the skills of the mixture model and individuals are both taken into consideration. The proposed MPM was tested using Innsbruck ensemble precipitation data and 6 h accumulated precipitation ensemble forecast data in east China from August to November 2017. Compared with raw forecasts and three state‐of‐the‐art post‐processing approaches, MPM showed improved performance for all verification scores. The quantitative and qualitative analyses of results in both cases indicate the potential and effectiveness of MPM for precipitation ensemble forecasting.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Mixture probabilistic model for precipitation ensemble forecasting

Yang

Zhang

et al. 2019

Quart J Royal Meteoro Soc

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“…4) Further comparison between the distributional regression scheme and the joint probability scheme is still needed. Although there are already some comparison works (Zhang et al 2017), other models belong to the two schemes still need to be compared. In this study, we used the model named ''censored regression with conditional heteroscedasticity'' (CRCH; Messner et al 2015) and a joint probability model similar to BJP as examples to compare the two schemes.…”

Section: Introductionmentioning

confidence: 99%

Factors Influencing the Performance of Regression-Based Statistical Postprocessing Models for Short-Term Precipitation Forecasts

Duan

Wang

2019

Weather and Forecasting

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Statistical postprocessing models can be used to correct bias and dispersion errors in raw precipitation forecasts from numerical weather prediction models. In this study, we conducted experiments to investigate four factors that influence the performance of regression-based postprocessing models with normalization transformations for short-term precipitation forecasts. The factors are 1) normalization transformations, 2) incorporation of ensemble spread as a predictor in the model, 3) objective function for parameter inference, and 4) two postprocessing schemes, including distributional regression and joint probability models. The experiments on the first three factors are based on variants of a censored regression model with conditional heteroscedasticity (CRCH). For the fourth factor, we compared CRCH as an example of the distributional regression with a joint probability model. The results show that the CRCH with normal quantile transformation (NQT) or power transformation performs better than the CRCH with log–sinh transformation for most of the subbasins in Huai River basin with a subhumid climate. The incorporation of ensemble spread as a predictor in CRCH models can improve forecast skill in our research region at short lead times. The influence of different objective functions (minimum continuous ranked probability score or maximum likelihood) on postprocessed results is limited to a few relatively dry subbasins in the research region. Both the distributional regression and the joint probability models have their advantages, and they are both able to achieve reliable and skillful forecasts.

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“…However, a mixture including the Bernoulli and gamma distributions to account for the mixed discrete/continuous nature of precipitation assumed by current studies is not a "true" mixture in the sense of uniform format but an addictive combination of two separate steps. At the same time, the corrected precipitation extremes relying on gamma distribution mapping or its analogies are insufficient to be captured.Recently, the censored shifted distribution has been proposed to uniformly and jointly model precipitation occurrence/nonoccurrence and intensity using a left-shifted continuous distribution instead of the multiple steps in mixed Bernoulli and gamma distributions (BerGam) [26][27][28]. The censoring and shifting change translates the fraction of the probability density function (PDF) which Remote Sens.…”

mentioning

confidence: 99%

“…Recently, the censored shifted distribution has been proposed to uniformly and jointly model precipitation occurrence/nonoccurrence and intensity using a left-shifted continuous distribution instead of the multiple steps in mixed Bernoulli and gamma distributions (BerGam) [26][27][28]. The censoring and shifting change translates the fraction of the probability density function (PDF) which Remote Sens.…”

mentioning

confidence: 99%

A Censored Shifted Mixture Distribution Mapping Method to Correct the Bias of Daily IMERG Satellite Precipitation Estimates

Xiong

Xia

et al. 2019

Remote Sensing

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Satellite precipitation estimates (SPE) provide useful input for hydrological modeling. However, hydrological modeling is frequently hindered by large bias and errors in SPE, inducing the necessity for bias corrections. Traditional distribution mapping bias correction of daily precipitation commonly uses Bernoulli and gamma distributions to separately model the probability and intensities of precipitation and is insufficient towards extremes. This study developed an improved distribution mapping bias correction method, which established a censored shifted mixture distribution (CSMD) as a transfer function when mapping raw precipitation to the reference data. CSMD coupled the censored shifted statistical distribution to jointly model both the precipitation occurrence probability and intensity with a mixture of gamma and generalized Pareto distributions to enhance extreme-value modeling. The CSMD approach was applied to correct the up-to-date SPE of Integrated Multi-satelliE Retrievals for Global Precipitation Measurement (GPM) with near-real-time "Early" run (IMERG-E) over the Yangtze River basin. To verify the hydrological response of bias-corrected IMERG-E, the streamflow of the Wujiang River basin was simulated using Ge´nie Rural with 6 parameters (GR6J) and Coupled Routing Excess Storage (CREST) models. The results showed that the bias correction using both BerGam (traditional bias correction combining Bernoulli with gamma distributions) and the improved CSMD could reduce the systematic errors of IMERG-E. Furthermore, CSMD outperformed BerGam in correcting overall precipitation (with the median of mean absolute errors of 2.46 mm versus 2.81 mm for CSMD and BerGam respectively, and the median of modified Nash-Sutcliffe efficiency of 0.39 versus 0.29) and especially in extreme values for uniform format and particular attention paid to extremes. In addition, the hydrological effect that CSMD correction exerted on IMERG-E, driving GR6J and CREST rainfall-runoff modeling, outperformed that of the BerGam correction. This study provides a promising integrated distribution mapping framework to correct the biased daily SPE, contributing to more reliable hydrological forecasts by informing accurate precipitation forcing. satellite precipitation estimates (SPE) have become potential alternatives to obtain high-resolution precipitation [3,4], as the densities of ground-based rain gauges are far below the standard of the World Meteorological Organization (WMO) recommendation, especially for mountainous areas featured with irregular precipitation (25 km 2 per gauge) [5]. Nevertheless, bias and errors, being generally larger than those of the rainfall gauge observations, are inherent in SPE products despite its fine spatial-temporal information [6][7][8][9]. Even the estimates of new-generation Global Precipitation Measurement (GPM) mission, i.e., the Integrated Multi-satelliE Retrievals for GPM (IMERG), which is anticipated to provide most accurate precipitation estimates currently, suffer from large bias [10,11]. Currently...

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Comparison of Probabilistic Quantitative Precipitation Forecasts from Two Postprocessing Mechanisms

Cited by 18 publications

References 29 publications

Mixture probabilistic model for precipitation ensemble forecasting

Mixture probabilistic model for precipitation ensemble forecasting

Factors Influencing the Performance of Regression-Based Statistical Postprocessing Models for Short-Term Precipitation Forecasts

A Censored Shifted Mixture Distribution Mapping Method to Correct the Bias of Daily IMERG Satellite Precipitation Estimates

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